Classwork 8

Map Visualization

Author

Byeong-Hak Choe

Published

March 26, 2025

Modified

March 27, 2025

Loading R packages

library(tidyverse)
library(skimr)
library(ggthemes)
library(socviz)
library(geofacet)

Part 1. Climate Opinion Map

The following data is for Part 1:

climate_opinion_long <- read_csv(
  'https://bcdanl.github.io/data/climate_opinion_2021.csv')

ABCDEFGHIJ0123456789

id <dbl>	GeoName <chr>	belief <chr>	perc <dbl>
1001	Autauga County, Alabama	happening	59.187
1001	Autauga County, Alabama	human	45.457
1003	Baldwin County, Alabama	happening	60.461
1003	Baldwin County, Alabama	human	44.678
1005	Barbour County, Alabama	happening	68.059
1005	Barbour County, Alabama	human	50.516
1007	Bibb County, Alabama	happening	57.593
1007	Bibb County, Alabama	human	42.594
1009	Blount County, Alabama	happening	52.504
1009	Blount County, Alabama	human	41.505

Variable Description

belief:
- human: Estimated percentage who think that global warming is caused mostly by human activities.
- happening: Estimated percentage who think that global warming is happening.

Question 1

Filter climate_opinion_long, so that climate_opinion_long has only estimated percentage of people who think that global warming is caused mostly by human activities.

Click to Check the Answer!

climate_opinion_long <- climate_opinion_long |>  
  filter(belief == 'human')

Question 2

Join the two data.frames, socviz::county_map and the resulting data.frame in Question 1.

Click to Check the Answer!

county_map <- socviz::county_map

county_map <- county_map |> 
  mutate(id = as.integer(id))

county_full <- county_map |> 
  left_join(climate_opinion_long)

Question 3

Replicate the following map.
- Do not use coord_map(projection = "albers", lat0 = 39, lat1 = 45).

Click to Check the Answer!

qtiles <- quantile(climate_opinion_long$perc, 
                   probs = c(0, 0.25, 0.5, 0.75, 1), 
                   na.rm = TRUE)

brk <- as.numeric(qtiles)

lab <- paste0(round(qtiles, 1), 
              "\n(", 
              c("Min", "25th", "50th", "75th", "Max"), 
              ")")

p1 <- ggplot(data = county_full) + 
  geom_polygon(mapping = aes(x = long, y = lat, group = group, 
                             fill = perc),
               color = "grey60", 
               linewidth = 0.1) 

p2 <- p1 + scale_fill_gradient2( 
  low = '#2E74C0',  
  high = '#CB454A',  
  mid = 'white', 
  na.value = "grey80",
  midpoint = quantile(climate_opinion_long$perc, .5, na.rm = T),
  breaks = brk,
  labels = lab,
  guide = guide_colorbar( direction = "horizontal",
                          barwidth = 25,
                          title.vjust = 1 )
) 

p <- p2 + labs(fill = "Percent\nBelief", title = "U.S. Climate Opinion, 2021",
               caption = "Sources: Yale Program on Climate Change Communication\n(https://climatecommunication.yale.edu/visualizations-data/ycom-us/)") +
  theme_map() +
  theme(plot.margin = unit( c(1, 1, 3.85, 0.5), "cm"),
        plot.title = element_text(size = rel(2),
                                  hjust = .5),
        plot.caption = element_text(hjust = 1),
        legend.position = c(0.5, -.15),
        legend.justification = c(.5,.5),
        aspect.ratio = .8,
        strip.background = element_rect( colour = "black",
                                         fill = "white",
                                         color = "grey80" )
  ) +
  guides(fill = guide_colourbar(direction = "horizontal", 
                                barwidth = 25,
                                title.vjust = 1)
         )
p

Part 2. Unemployment Rate Maps with `geofacet::facet_geo()`

The following data is for Part 2:

unemp_house_prices <- read_csv(
  'https://bcdanl.github.io/data/unemp_house_prices.csv')

ABCDEFGHIJ0123456789

date <date>	state <chr>	unemploy_perc <dbl>	house_price_index <dbl>	house_price_perc <dbl>
1976-01-01	Alabama	6.7	37.13829	4.040541e-02
1976-02-01	Alabama	6.7	37.75644	5.180597e-02
1976-03-01	Alabama	6.6	38.27632	5.930233e-02
1976-04-01	Alabama	6.5	38.59613	5.958458e-02
1976-05-01	Alabama	6.4	38.74052	5.572338e-02
1976-06-01	Alabama	6.5	38.79723	5.359368e-02
1976-07-01	Alabama	6.6	38.85695	5.741669e-02
1976-08-01	Alabama	6.8	38.91870	6.592198e-02
1976-09-01	Alabama	6.9	38.95837	7.456134e-02
1976-10-01	Alabama	7.0	39.01876	7.967682e-02

Use geom_area(), geom_line(), and facet_geo(~state, labeller = adjust_labels) to replicate the following figure

adjust_labels <- as_labeller(
  function(x) {
    case_when(
      x == "New Hampshire" ~ "N. Hampshire",
      x == "District of Columbia" ~ "DC",
      TRUE ~ x
    )
  }
)

Click to Check the Answer!

unemp_house_prices |> 
  filter(
    date >= ymd("2008-01-01")
  ) |>
  ggplot(aes(date, unemploy_perc)) + 
  geom_area(fill = "#56B4E9", alpha = 0.7) +
  geom_line() + 
  scale_y_continuous(
    name = "unemployment rate",
    limits = c(0, 16),
    breaks = c(0, 5, 10, 15),
    labels = c("0%", "5%", "10%", "15%")
  ) +
  scale_x_date(
    name = NULL,
    breaks = ymd(c("2009-01-01", "2011-01-01", 
                   "2013-01-01", "2015-01-01", "2017-01-01")),
    labels = c("'09", "'11", "'13", "'15", "'17")
  ) +
  facet_geo(~state, labeller = adjust_labels) +
  theme(
    strip.text = element_text(
      margin = margin(3, 3, 3, 3)
    ),
    axis.line.x = element_blank()
  )

Loading R packages

Part 1. Climate Opinion Map

Variable Description

Question 1

Question 2

Question 3

Part 2. Unemployment Rate Maps with geofacet::facet_geo()

Part 2. Unemployment Rate Maps with `geofacet::facet_geo()`